S. Anbazhagana, and S. Karthikumarb,*
| 1. Dey, S., Bhattacharyya, S. and Maulik, U.Quantum behaved multi-objective PSO and ACO optimization for multi-level thresholding. In
2. Bhandari A.K., Kumar A., andSingh G.K.Tsallis entropy based MT for colored satellite image segmentation using evolutionary algorithms.
3. Otsu N.A threshold selection method from gray-level histograms.
4. Kapur, J.N., Sahoo, P.K. and Wong, A.K.A new method for gray-level picture thresholding using the entropy of the histogram.
5. Huang, L.K. and Wang, M.J.J. Image thresholding by minimizing the measures of fuzziness.
6. Qiao Y., Hu Q., Qian G., Luo S. and Nowinski W.L.Thresholding based on variance and intensity contrast.
7. Li, X., Zhao, Z. and Cheng, H.D.Fuzzy entropy threshold approach to breast cancer detection.
8. Li, C.H. and Tam, P.K.S. An iterative algorithm for minimum cross entropy thresholding.
9. Li, K. and Tan, Z.An improved flower pollination optimizer algorithm for multilevel image thresholding.
10. Li Y., Bai X., Jiao L. and Xue Y.Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation.
11. Kittler, J. and Illingworth, J., 1986. Minimum error thresholding.
12. De Albuquerque, M.P., Esquef, I.A. and Mello, A.G. Image thresholding using Tsallis entropy.
13. Zarezadeh, S. and Asadi, M.Results on residual Rényi entropy of order statistics and record values.
14. Hammouche, K., Diaf, M. and Siarry, P.A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem.
15. Alihodzic, A. and Tuba, M.Improved bat algorithm applied to multilevel image thresholding.The Scientific World Journal, 2014.
16. Sri Madhava Raja, N., Rajinikanth, V. and Latha, K. Otsu based optimal multilevel image thresholding using firefly algorithm.Modelling and Simulation in Engineering, 2014.
17. Oliva D., Cuevas E., Pajares G., Zaldivar D. and Perez-Cisneros, M. Multilevel thresholding segmentation based on harmony search optimization.Journal of Applied Mathematics, 2013.
18. Liu Y., Mu C., Kou W. and Liu J.Modified particle swarm optimization-based multilevel thresholding for image segmentation.
19. Liu Y., Liu J., Tian L. and Ma L.Hybrid artificial root foraging optimizer based multilevel threshold for image segmentation.Computational intelligence and neuroscience, 2016.
20. Agarwal P., Singh R., Kumar S. and Bhattacharya M.Social spider algorithm employed multi-level thresholding segmentation approach. In
21. Ouadfel, S. and Taleb-Ahmed, A. Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study.
22. Chen K., Zhou Y., Zhang Z., Dai M., Chao Y. and Shi J.Multilevel image segmentation based on an improved firefly algorithm.Mathematical Problems in Engineering, 2016.
23. Cao L.L., Ding S., Fu X.W. and Chen L.Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm.
24. Pal S.S., Kumar S., Kashyap M., Choudhary Y. and Bhattacharya M.Multi-level thresholding segmentation approach based on spider monkey optimization algorithm. In
25. Panda R., Agrawal S., Samantaray L. and Abraham A.An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques.
26. Abd El Aziz, M., Ewees, A.A. and Hassanien, A.E. Hybrid swarms optimization based image segmentation. In
27. Abd El Aziz, M., Ewees, A.A. and Hassanien, A.E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation.
28. Abd Elaziz, M., Ewees, A.A. and Oliva, D. Hyper-heuristic method for multilevel thresholding image segmentation.
29. Khairuzzaman A.K.M. and Chaudhury, S. Multilevel thresholding using grey wolf optimizer for image segmentation.
30. Rao, R.V., Savsani, V.J. and Vakharia, D.P.Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems.
31. Rao, R.V., Savsani, V.J. and Balic, J.Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems.
32. Venkata Rao, R. and Kalyankar, V.D. Parameter optimization of machining processes using a new optimization algorithm.
33. Venkata Rao, R. and Patel, V. Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms.
|||Sanjay Kumar Ahuja, Manoj Kumar Shukla, and Kiran Kumar Ravulakollu. Optimized Deep Learning Framework for Detecting Pitting Corrosion based on Image Segmentation [J]. Int J Performability Eng, 2021, 17(7): 627-637.|
|||S. Anbazhagan and Bhuvaneswari Ramachandran. Ameliorating Vertically Bundled Electricity Price Prediction Exclusively from ICMLP Network [J]. Int J Performability Eng, 2021, 17(4): 364-370.|
|||Chulei Zhang, Honghua Cui, Yizhang Wang, Tiantian Zhao, and You Zhou. LDKM: An Improved K-Means Algorithm with Linear Fitting Density Peak [J]. Int J Performability Eng, 2020, 16(3): 454-461.|
|||Youfen Chen. Improved Particle Swarm Optimization Algorithm for Image Segmentation [J]. Int J Performability Eng, 2020, 16(3): 482-489.|
|||Zhenggang Wang, and Guanling Wang. Triplanar Convolutional Neural Network for Automatic Liver and Tumor Image Segmentation [J]. Int J Performability Eng, 2018, 14(12): 3151-3158.|